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Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving
| Content Provider | MDPI |
|---|---|
| Author | Lu, Bing He, Hongwen Yu, Huilong Wang, Hong Li, Guofa Cao, Dongpu |
| Copyright Year | 2020 |
| Description | The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. |
| Starting Page | 7197 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20247197 |
| Journal | Sensors |
| Issue Number | 24 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-16 |
| Access Restriction | Open |
| Subject Keyword | Sensors Transportation Science and Technology Potential Field Sigmoid Curve Path Planning Autonomous Vehicles |
| Content Type | Text |
| Resource Type | Article |
